Asthma and risk of glioma: a population-based case–control study. Issue 6 (17th June 2019)

• Record Type: Journal Article
• Authors: Kaur, Harsheen; Lachance, Daniel H; Ryan, Conor S; Sheen, Youn Ho; Seol, Hee Yun; Wi, Chung-Il; Sohn, Sunghwan; King, Katherine S; Ryu, Euijung; Juhn, Young
• Is Part Of: BMJ open. Volume 9:Issue 6(2019)
• Publication Date: 2019-06-17
• Subjects: asthma -- epidemiology -- allergy; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2018-025746
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: Literature suggests an inconsistent, but largely inverse, association between asthma and risk of glioma, which is primarily due to methodological inconsistency in sampling frame and ascertainment of asthma. The objective of the study was to clarify the association between asthma and risk of glioma by minimising methodological biases (eg, recall and detection bias). Design: A population-based case–control study. Setting: General population in Olmsted County, Minnesota, USA. Participants: All eligible biopsy-proven incident glioma cases (1995–2014) and two sets of controls among residents matched to age and sex (first set: community controls without glioma; second set: MRI-negative controls from the same community). Methods: The predetermined asthma criteria via medical record review were applied to ascertain asthma status of cases and controls. History of asthma prior to index date was compared between glioma cases and their matched controls using conditional logistic regression models. Propensity score for asthma status was adjusted for multivariate analysis. Results: We enrolled 135 glioma cases (median age at index date: 53 years) and 270 controls. Of the cases, 21 had a history of asthma (16%), compared with 36 of MRI controls (27%) (OR (95% CI) 0.48 (0.26 to 0.91), p=0.03). With MRI controls, an inverse association between asthma and risk of glioma persisted after adjusting for the propensity score for asthma status, but did not reach statistical significance probably due to the lack of statistical power (OR (95% CI) 0.48 (0.21 to 1.09); p=0.08). Based on comparison of characteristics of controls and cases, community controls seem to be more susceptible to a detection bias. Conclusions: While differential detection might account for the association between asthma and risk of glioma, asthma may potentially pose a protective effect on risk of glioma. Our study results need to be replicated by a larger study.